Product and AI Strategy
Create category-leading products to accelerate revenue, delight your customers and increase your company valuation.
We refine your agile product roadmap and define your
We refine your product direction and agile roadmap.
With the rapid change in today's technology markets, strategy development must become smarter, more pragmatic, more agile and faster.
With our deep product and engineering expertise gained after managing, launching and growing dozens of B2B software products, services and technologies, we work with you to hone your product definition and strategy for both internal and external use, based on your unique category blueprint.
We vet your value proposition, personas and product definition. Then we help refine the agile product roadmap and customer validation plan. We help discover new opportunities, analyze the competitive landscape, and validate product/market fit to focus your engineering team on the right priorities to get to revenue faster.
We define your AI strategy to improve your value proposition and drive revenue.
At WorksMachine we are passionate about our mission to help companies improve their growth, or return to a growth path, by applying AI/ML techniques to create new products or to improve the value proposition of existing products.
We apply cognitive technologies to support the following:
Create new products or offerings
Boost product performance
Optimize internal business operations
Improve customer processses
Reduce head count
Product, company, data and models complement each other to drive your AI strategy.
What problem are you solving?
Can it benefit from AI?
What are our strengths as a company?
What would it take to add AI?
Do you have adequate high quality training data?
What algorithms are appropriate for your product?
Our AI engagement methodology.
We start with an AI expertise audit to understand what gaps there are in your team — and what resources are needed to fill those gaps.
Using our methodology, we drive to a deployable AI solution:
Data strategy to ensure you have access to high quality data
Data prep and labeling
Selection of the right algorithms to model the data
Define the training data set strategy
Drive workflow feedback and iteration of the modeling
Plan for model deployment and scaling
Learn about growth acceleration.